trajectory inference

Computational model of developmental self-assembly of a cortical microcircuit

The paper develops a computational model to simulate the self-assembly of cortical microcircuits from progenitors, reproducing experimentally measured connectivity without prescribing individual connections.

FLASH radiotherapy drives intestinal regeneration via macrophage-derived WNT4 signaling and attenuated T-cell inflammation

This study shows FLASH radiotherapy promotes intestinal regeneration by preserving stem cells, enhancing WNT4 signaling, and reducing T-cell inflammation in mice.

Disentangled generative modeling of single-cell perturbation responses across dynamic cellular states

This paper introduces PertVI, a deep generative model that disentangles treatment effects from cellular state and batch effects in single-cell perturbation data, revealing state-specific transcriptional responses.

Subcellular Spatial Profiling Unveils Links Between Neuritic Plaques with Selective Tau Vulnerability in Alzheimer's Disease

The paper uses subcellular spatial transcriptomics to identify why some neurons in Alzheimer's disease accumulate tau while others resist it, finding differences in molecular programs and neuron-glia interactions.

Ergodic breakdown of brain dynamics under chronnectome complexity across cognitive states and age

The paper introduces a model to classify brain dynamics based on chronnectome complexity, showing high accuracy in distinguishing cognitive states and age-related changes.

scHiR learns joint cellular representations from paired single-cell transcriptome and 3D genome data

scHiR learns joint representations from paired scRNA-seq and scHi-C data, improving clustering and trajectory inference while recovering input signals.

Calibrating developmental dynamics from single-cell atlases: lineage paths outrun least-action routes

The paper introduces LADD to measure lineage path deviations from inferred developmental dynamics in single-cell RNA-seq data and finds lineages move faster than optimal paths.

Label-free flow operators and stability operators from single-cell time courses: what local dynamical operators can and cannot identify

This paper introduces scJDO2, a method to separate score and flow operators from single-cell time courses, evaluating their ability to identify unstable cells and bifurcations in hematopoiesis.

Virtual dynamics of spatiotemporal single-cell interactions

CytoBridge reconstructs continuous virtual cell dynamics from sparse spatial transcriptomic data across development, regeneration, and neurodegeneration.

scDisentangle: sequential disentanglement for single-cell perturbation response prediction

scDisentangle predicts single-cell transcriptional responses to perturbations by decomposing expression into residual components, outperforming existing methods in cytokine stimulation, infection, and genetic perturbations.

Reconstruction of Cellular Trajectories with Biologically Interpretable States

The paper proposes a Latent Cell-State Model to reconstruct cellular trajectories by inferring hidden cell states and generating gene expression profiles at unobserved timepoints.

Variation in the embryonic cell lineages of Caenorhabditis elegans natural populations

The paper quantifies developmental variation in C. elegans natural populations using embGAN, finding genotype-specific differences in cell-cycle timing that exceed within-strain variation.

Response to azacitidine and the TIM-3 inhibitor, sabatolimab, in MDS and CMML is associated with distinct immune and antigen-presentation states

This study identifies immune and antigen-presentation states associated with response to azacitidine and sabatolimab in MDS and CMML using single-cell RNA sequencing.

Discovering compositional, interpretable structure in social behavior

This paper introduces a method to model social behavior using decomposed linear dynamical systems, showing it captures complex, compositional actions more effectively than existing methods.

Discovering Prognostic Risk Strata and Heterogeneous Progression Routes from Cross-Sectional Survival Data

GOTSurv models patient-specific disease progression from cross-sectional survival data, identifying heterogeneous risk strata and individualized progression trajectories without predefined risk cut-offs.

Thalamic stereo-EEG detects sleep stages N2 and N3 with discrimination approaching scalp EEG

This study shows thalamic stereo-EEG can detect sleep stages N2 and N3 with high accuracy, comparable to scalp EEG, using machine learning classifiers.

ALIGN: A modular framework for quantitative analysis of longitudinal development in Caenorhabditis elegans

ALIGN provides a modular framework for analyzing longitudinal development in C. elegans, enabling accurate segmentation, quality control, and motion correction across imaging conditions.

Evolution of Vesicle Shape Parameters Based on A Deep-Learning Based Image Analysis Framework Over Extended Time Periods

The paper uses a deep-learning framework to analyze shape dynamics of GUVs over time, measuring morphometric changes and budding behavior.

BioTrajX: evaluating biological plausibility in single-cell trajectory inference

BioTrajX evaluates biological plausibility of single-cell trajectories using three metrics to compare different trajectory inference results.

PHASOR: An Open-Source Pipeline for Hidden Markov Model Analysis of Stimulus-Locked Retinal Multielectrode-Array Population Activity

PHASOR is a pipeline that analyzes retinal multielectrode-array data using hidden Markov models to link stimulus timing with population activity states.

LOTUS: lineage-aware optimal transport reconstructs continuous clonal fate dynamics

LOTUS reconstructs continuous clonal fate dynamics using lineage-aware optimal transport, improving lineage consistency and fate prediction in single-cell data.

Graph neural network approach for inferring neuronal connectivity

This paper introduces CircuitGNN, a graph neural network that infers neural connectivity from population activity, showing high accuracy and robustness across different circuit sizes and recording conditions.

A phase decoder for reconstructing missing muscle activity

This paper introduces a phase decoder that reconstructs missing muscle activity from sparse EMG signals, achieving accurate phase estimation and muscle activation reconstruction in real-time.

Path knockout: an analysis tool for connectome-driven circuit discovery

Path knockout identifies dominant multi-synaptic pathways in connectomes by quantifying the impact of neuron removal on connection strength, outperforming existing methods in Drosophila circuits.

NeuralFieldManifold: Uncovering the Manifold Geometry of Local Field Potentials

This paper introduces a geometric framework for analyzing local field potentials (LFPs) by modeling their dynamics as low-dimensional K-tori and validates it across neural datasets.

Wearable Estimation of Knee Joint Contact Forces: SiMPLeSENSE, a Simulated-IMU Pipeline Validated Against In Vivo Loads

The paper develops SiMPLeSENSE, a simulated-IMU pipeline that estimates knee joint contact forces using motion capture and EMG, validated against in vivo measurements with high accuracy.

A single-cell multimodal foundation model enables cross modality analysis between transcriptome and chromatin

MUTUAL enables cross-modality analysis between transcriptome and chromatin by learning a shared RNA-ATAC space from multiome data without refitting.

Mechanistic gene-regulation-based trajectory inference with bowside

The paper introduces bowside, a neural model for trajectory inference and gene regulatory network reconstruction in single-cell RNA sequencing data, showing it recovers known regulatory motifs in hematopoiesis and reprogramming.

Digital twins forecast individual immune trajectories

The paper introduces ImmuneTwins, a digital twin framework that personalizes immune response models for individuals, improving prediction accuracy of immune trajectory changes.

Data-driven gait cycle decomposition based on whole-body coordination dynamics

This study decomposes gait cycles using a dynamic system framework to identify coordination patterns in human locomotion.